Do Lateral Views Help Automated Chest X-ray Predictions?

Autor:	Bertrand, Hadrien, Hashir, Mohammad, Cohen, Joseph Paul
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Most convolutional neural networks in chest radiology use only the frontal posteroanterior (PA) view to make a prediction. However the lateral view is known to help the diagnosis of certain diseases and conditions. The recently released PadChest dataset contains paired PA and lateral views, allowing us to study for which diseases and conditions the performance of a neural network improves when provided a lateral x-ray view as opposed to a frontal posteroanterior (PA) view. Using a simple DenseNet model, we find that using the lateral view increases the AUC of 8 of the 56 labels in our data and achieves the same performance as the PA view for 21 of the labels. We find that using the PA and lateral views jointly doesn't trivially lead to an increase in performance but suggest further investigation. Comment: 3 pages and 1 figure. Under review as extended abstract at MIDL 2019 [arXiv:1907.08612]
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1904.08534 Zobrazit plný text záznamu View this record from Arxiv